Developing novel quantitative imaging analysis schemes based machine learning for cancer research

The computer-aided detection (CAD) scheme is a developing technology in the medical imaging field, and it attracted extensive research interest in recent years. In this dissertation, I investigated the feasibility of developing several new novel CAD schemes for different cancer research purposes. First, I investigated the feasibility of identifying a new quantitative imaging marker based on false-positives generated by a computer-aided detection (CAD) scheme to predict short-term breast cancer risk. For this study, an existing CAD scheme was applied “as is” to process each image. From CAD-generated results, some detection features were computed from each image. Two logistic regression models were then trained and tested using a leave-one-case-out cross-validation method to predict each testing case's likelihood of being positive in the next subsequent screening. This study demonstrated that CAD-generated false-positives contain valuable information to predict short-term breast cancer risk. Second, I identified and applied quantitative imaging features computed from ultrasound images of athymic nude mice to predict tumor response to treatment at an early stage. For this study, a CAD scheme was developed to perform tumor segmentation and image feature analysis. The study demonstrated the feasibility of extracting quantitative image features from the ultrasound images taken at an early treatment stage to predict tumor response to therapies. Last, I optimized a machine learning model for predicting peritoneal metastasis in gastric cancer. For this purpose, I have developed a CAD scheme to segment the tumor volume and extract quantitative image features automatically. Then, I reduced the dimensionality of features with a new method named random projection to optimize the model's performance. Finally, the gradient boosting machine model was applied along with a synthetic minority oversampling technique to predict peritoneal metastasis risk. Results suggested that the random projection method yielded promising results in improving the accuracy performance in peritoneal metastasis prediction. In summary, in my Ph.D. studies, I have investigated and tested several innovative approaches to develop different CAD schemes and identify quantitative imaging markers with high discriminatory power in various cancer research applications. Study results demonstrated the feasibility of applying CAD technology to several new application fields, which can help radiologists and gynecologists improve accuracy and consistency in disease diagnosis and prognosis assessment of using the medical image

Automated Quantification of Pneumonia Infected Volume in Lung CT Images: A Comparison with Subjective Assessment of Radiologists

Objective: To help improve radiologists’ efficacy of disease diagnosis in reading computed tomography (CT) images, this study aims to investigate the feasibility of applying a modified deep learning (DL) method as a new strategy to automatically segment disease-infected regions and predict disease severity. Methods: We employed a public dataset acquired from 20 COVID-19 patients, which includes manually annotated lung and infections masks, to train a new ensembled DL model that combines five customized residual attention U-Net models to segment disease infected regions followed by a Feature Pyramid Network model to predict disease severity stage. To test the potential clinical utility of the new DL model, we conducted an observer comparison study. First, we collected another set of CT images acquired from 80 COVID-19 patients and process images using the new DL model. Second, we asked two chest radiologists to read images of each CT scan and report the estimated percentage of the disease-infected lung volume and disease severity level. Third, we also asked radiologists to rate acceptance of DL model-generated segmentation results using a 5-scale rating method. Results: Data analysis results show that agreement of disease severity classification between the DL model and radiologists is >90% in 45 testing cases. Furthermore, >73% of cases received a high rating score (≥4) from two radiologists. Conclusion: This study demonstrates the feasibility of developing a new DL model to automatically segment disease-infected regions and quantitatively predict disease severity, which may help avoid tedious effort and inter-reader variability in subjective assessment of disease severity in future clinical practice

Correlation of imaging and plasma based biomarkers to predict response to bevacizumab in epithelial ovarian cancer (EOC)

PurposeIncreasing measures of adiposity have been correlated with poor oncologic outcomes and a lack of response to anti-angiogenic therapies. Limited data exists on the impact of subcutaneous fat density (SFD) and visceral fat density (VFD) on oncologic outcomes. This ancillary analysis of GOG-218, evaluates whether imaging markers of adiposity were predictive biomarkers for bevacizumab (bev) use in epithelial ovarian cancer (EOC).Patients and methodsThere were 1249 patients (67%) from GOG-218 with imaging measurements. SFD and VFD were calculated utilizing Hounsfield units (HU). Proportional hazards models were used to assess the association between SFD and VFD with overall survival (OS).ResultsIncreased SFD and VFD showed an increased HR for death (HR per 1-SD increase 1.12, 95% CI:1.05-1.19 p = 0.0009 and 1.13, 95% CI: 1.05-1.20 p = 0.0006 respectively). In the predictive analysis for response to bev, high VFD showed an increased hazard for death in the placebo group (HR per 1-SD increase 1.22, 95% CI: 1.09-1.37; p = 0.025). However, in the bev group there was no effect seen (HR per 1-SD increase: 1.01, 95% CI: 0.90-1.14) Median OS was 45 vs 47 months in the VFD low groups and 36 vs 42 months in the VFD high groups on placebo versus bev, respectively.ConclusionHigh VFD and SFD have a negative prognostic impact on patients with EOC. High VFD appears to be a predictive marker of bev response and patients with high VFD may be more likely to benefit from initial treatment with bev